Identity Management for Web2 and Web3 Environments

ABSTRACT

Systems and methods for profile management across Web2 and Web3 environments can include a user profile database. The user profile database can store a plurality of keys associated with a plurality of blockchains. The systems and methods can process a user verification request, determine a particular blockchain associated with the user verification request, determine a particular key associated with the particular blockchain, and provide the particular key to the particular blockchain.

RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 63/394,775, filed Aug. 3, 2022. U.S. ProvisionalPatent Application No. 63/394,775 is hereby incorporated by reference inits entirety.

FIELD

The present disclosure relates generally to identity management for aplurality of accounts including accounts associated with differentblockchain environments. More particularly, the present disclosurerelates to a profile management system that facilitates user-specificaccess to a plurality of platforms with a plurality of differentplatform specific accounts managed based on a singular managementprofile.

BACKGROUND

A user can have a plurality of different profiles with a plurality ofdifferent login criteria. Therefore, a user may have to remember and/orstore a large variety of datasets that may be associated with a largevariety of different platforms. Additionally, the creation of thedifferent accounts can be tedious and at times redundant.

The introduction of Web3 environments can add an additional hurdle asthe login procedures can differ from the login procedures of a Web2environment. The login verification for blockchain platforms can bedifficult, and the various blockchain wallets may be difficult tomanage.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or can be learned fromthe description, or can be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to a computingsystem. The system can include one or more processors and one or morenon-transitory computer-readable media that collectively storeinstructions that, when executed by the one or more processors, causethe computing system to perform operations. The operations can includeobtaining a user verification request. The user verification request canbe associated with a particular blockchain of a plurality of differentblockchains. The operations can include obtaining user profile databased on the user verification request. The user profile data can beassociated with a particular user. In some implementations, the userprofile data can include a plurality of keys associated with theplurality of different blockchains. The operations can includedetermining a particular key of the plurality of keys associated withthe particular blockchain. The operations can include providing theparticular key to a blockchain computing system. The blockchaincomputing system can be associated with the particular blockchain.

Another example aspect of the present disclosure is directed to acomputer-implemented method. The method can include obtaining, by acomputing system including one or more processors, a user verificationrequest. The user verification request can be associated with aparticular blockchain. The method can include obtaining, by thecomputing system, user profile data based on the user verificationrequest. The user profile data can be associated with a particular user.In some implementations, the user profile data can include a pluralityof identification datasets associated with the plurality of differentweb platforms. The method can include processing, by the computingsystem, the user profile data to determine a particular key associatedwith the particular blockchain. The method can include providing, by thecomputing system, the particular key to a blockchain computing system.The blockchain computing system can be associated with the particularblockchain.

Another example aspect of the present disclosure is directed to one ormore non-transitory computer-readable media that collectively storeinstructions that, when executed by one or more computing devices, causethe one or more computing devices to perform operations. The operationscan include obtaining a user verification request. The user verificationrequest can be associated with a particular blockchain of a plurality ofdifferent blockchains. The operations can include obtaining user profiledata based on the user verification request. In some implementations,the user profile data can be associated with a particular user. The userprofile data can be managed with a profile management extension. Theuser profile data can include a plurality of blockchain profile datasetsassociated with the plurality of different blockchains. The operationscan include processing the user profile data to determine a particularkey associated with the particular blockchain. In some implementations,the particular key can be associated with a particular blockchainprofile dataset of the plurality of blockchain profile datasets. Theoperations can include providing the particular key to a blockchaincomputing system. The blockchain computing system can be associated withthe particular blockchain.

Other aspects of the present disclosure are directed to various systems,apparatuses, non-transitory computer-readable media, user interfaces,and electronic devices.

These and other features, aspects, and advantages of various embodimentsof the present disclosure will become better understood with referenceto the following description and appended claims. The accompanyingdrawings, which are incorporated in and constitute a part of thisspecification, illustrate example embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1A depicts a block diagram of an example computing system thatperforms identity management according to example embodiments of thepresent disclosure.

FIG. 1B depicts a block diagram of an example computing device thatperforms identity management according to example embodiments of thepresent disclosure.

FIG. 2 depicts a block diagram of an example profile management systemaccording to example embodiments of the present disclosure.

FIG. 3 depicts a block diagram of an example key determination systemaccording to example embodiments of the present disclosure.

FIG. 4 depicts a block diagram of an example user profile databaseaccording to example embodiments of the present disclosure.

FIG. 5 depicts a block diagram of an example user dashboard according toexample embodiments of the present disclosure.

FIG. 6 depicts a flow chart diagram of an example method to perform userverification according to example embodiments of the present disclosure.

FIG. 7 depicts a flow chart diagram of an example method to performprofile generation according to example embodiments of the presentdisclosure.

FIG. 8 depicts a flow chart diagram of an example method to performtransaction authorization according to example embodiments of thepresent disclosure.

FIG. 9A depicts a block diagram of an example computing system thatperforms identity management according to example embodiments of thepresent disclosure.

FIG. 9B depicts a block diagram of an example computing device thatperforms identity management according to example embodiments of thepresent disclosure.

FIG. 9C depicts a block diagram of an example computing system thatperforms identity management according to example embodiments of thepresent disclosure.

Reference numerals that are repeated across plural figures are intendedto identify the same features in various implementations.

DETAILED DESCRIPTION Overview

Generally, the present disclosure is directed to systems and methods foridentity management for blockchain interactions. In particular, thesystems and methods disclosed herein can leverage a profile managementsystem for managing a plurality of keys (e.g., private keys and/orpublic keys). For example, the systems and methods can utilize theprofile management system to interact with a plurality of differentblockchains and/or Web2 platforms.

The systems and methods can leverage web identities on differentblockchains. In some implementations, the systems and methods can link aplurality of different accounts under a single profile. For example, theprofile can be utilized for logging into different blockchains.

In some implementations, the systems and methods can include a centralidentity management for Web2 and Web3. The central identity managementcan manage public keys and/or private keys.

For example, the systems and methods can include obtaining a userverification request. The user verification request can be associatedwith a particular blockchain of a plurality of different blockchains.User profile data can be obtained based on the user verificationrequest. The user profile data can be associated with a particular user.In some implementations, the user profile data can include a pluralityof keys associated with the plurality of different blockchains. Thesystems and methods can include determining a particular key of theplurality of keys associated with the particular blockchain. Theparticular key can be provided to a blockchain computing system. Theblockchain computing system can be associated with the particularblockchain.

In particular, the systems and methods disclosed herein can leverage aprofile management system to provide an intermediary system that caninterface with a plurality of different Web2 and Web3 platforms. Theprofile management system can include a database of management profiles,and each management profile can be associated with one or more accountprofiles such that a user can log into the management profile tointeract with each of their one or more account profiles. The accountprofile data may be manually input by the user. Alternatively and/oradditionally, a user may log into and/or generate a management profile.The user can then input one or more selections to the profile managementsystem. The profile management system can then interface with an accountinterface of a third party computing system in order to generate anaccount with the third party web platform. Data associated with theaccount can then be stored in the database of the profile managementsystem to provide seamless access to the third party web platform.

In some implementations, the profile management system can utilize themanagement profile to obtain data associated with a plurality ofnon-fungible tokens associated with the user to provide a centralizedinterface for viewing and/or interacting with the plurality ofnon-fungible tokens for the tokens on a plurality of differentblockchains. The centralization of the non-fungible token data pointscan allow for the data to be utilized for providing more user-specificexperience for search predictions and suggestions.

The systems and methods can obtain a user verification request. The userverification request can be associated with a particular blockchain of aplurality of different blockchains. In some implementations, obtainingthe user verification request can be based on providing input data to ablockchain computing system. The user verification request can beobtained from a blockchain computing system. Alternatively and/oradditionally, the user verification request may be obtained from ablockchain computing system. The user verification request can bedescriptive of a request for one or more particular keys associated witha particular blockchain. Additionally and/or alternatively, the userverification request can include a request for account information(e.g., an account ID, a username, an email, a password, a hash, and/oran IP address).

User profile data can then be obtained based on the user verificationrequest. The user profile data can be associated with a particular user.In some implementations, the user profile data can include a pluralityof keys associated with the plurality of different blockchains. The userprofile data can be stored by a server computing system. In someimplementations, the user verification request can be obtained via a webbrowser application. Additionally and/or alternatively, providing theparticular key can include providing the particular key via anapplication extension. The application extension can be a browserextension that interfaces with a browser application. Alternativelyand/or additionally, providing the particular key can includeinstructing an application programming interface to interface with theparticular blockchain. In some implementations, the user profile datacan include login information for a plurality of web platforms. The userprofile data can be managed by a profile management system thatfacilitates the storage, retrieval, and transmission of one or moreprivate keys. In some implementations, the plurality of keys can bestored with one or more cryptographic techniques.

In some implementations, obtaining the user profile data based on theuser verification request can include accessing a user profile database.The user profile database can include profile information for aplurality of different users. Additionally and/or alternatively, theuser profile database can be accessed via a profile management system.The profile management system can include an intermediary system forobtaining user information and interacting with a plurality of differentweb platforms and/or account services.

The systems and methods can determine a particular key of the pluralityof keys associated with the particular blockchain. Determining theparticular key can include processing the user profile data to determinea subset of the user profile data that is associated with the particularblockchain. The subset of the user profile data can then be processed toidentify the particular key. In some implementations, the determinationcan be performed by a machine-learned model. Alternatively and/oradditionally, the determination can be performed based on one or moredeterministic functions and/or heuristics. The identification can bebased on the data structure associated with the particular key and/orbased on one or more annotations or indexing techniques.

In some implementations, determining the particular key of the pluralityof keys associated with the particular blockchain can includedetermining the particular blockchain based on the user verificationrequest and determining the particular key based on the particularblockchain.

The particular key can be provided to a blockchain computing system. Theblockchain computing system can be associated with the particularblockchain. Providing the particular key can include causing anapplication programming interface to interface with the blockchaincomputing system.

In some implementations, the systems and methods can include obtainingblockchain data from the blockchain computing system in response toproviding the particular key to the blockchain computing system. Theblockchain data may be obtained via an application programming interfaceand/or via a blockchain node.

Alternatively and/or additionally, the systems and methods can includeobtaining one or more user inputs. The systems and methods can determinethe one or more user inputs are associated with a blockchain transactionrequest. The blockchain transaction request can be provided to theblockchain computing system. Transaction data can then be obtained inresponse to providing the particular key and the blockchain transactionrequest.

The verification request can be obtained in response to a user computingsystem accessing a Web3 environment. For example, the user verificationrequest can be obtained in response to a user computing systeminteracting with a web platform. The user verification request can beassociated with a particular blockchain. User profile data can beobtained based on the user verification request. The user profile datacan be associated with a particular user. In some implementations, theuser profile data can include a plurality of identification datasetsassociated with the plurality of different web platforms. The userprofile data can be processed to determine a particular key associatedwith the particular blockchain. In some implementations, the particularkey can be provided to a blockchain computing system. The blockchaincomputing system can be associated with the particular blockchain.

A user verification request can be obtained from at least one of a usercomputing system and/or a third party computing system. The userverification request can be associated with a particular blockchain. Theuser verification request can be associated with a login portalassociated with the particular blockchain. In some implementations, theuser verification request can be generated based on the detection of oneor more input fields in the login portal. The user verification requestcan be descriptive of a request for an input for each of the one or moreinput fields.

User profile data can be obtained based on the user verificationrequest. The user profile data can be associated with a particular user.In some implementations, the user profile data can include a pluralityof identification datasets associated with the plurality of differentweb platforms. The plurality of different web platforms can include aplurality of different blockchain platforms. In some implementations,the plurality of different web platforms can include one or more socialmedia platforms. Additionally and/or alternatively, each identificationdataset of the plurality of identification datasets can include logininformation associated with a respective web platform. An identificationdataset associated with a blockchain can include one or more keys (e.g.,one or more private keys and/or one or more public keys), one or moretoken IDs, one or more hashes, and/or one or more asset-specificdatasets (e.g., data associated with non-fungible tokens and/orcryptocurrency). Additionally and/or alternatively, the user profiledata can include an email address and/or other communication data. Theemail address and/or the other communication data can be leveraged forone or more users to email other users associated with a particularnon-fungible token community. The communication data can therefore beleveraged for community building.

In some implementations, a profile management system can provide acommunication interface that obtains communication requests from a firstuser, determines an intended audience, obtains communication dataassociated with the intended audience (e.g., one or more second users),and provides a communication notification to the intended audience. Thecommunication can be performed without revealing the communication dataof the first user or the one or more second users. The profilemanagement system can therefore keep the communication data private,while still allowing users to build a community.

The user profile data can then be processed to determine a particularkey associated with the particular blockchain. In some implementations,processing the user profile data to determine the particular keyassociated with the particular blockchain can include determining aparticular identification dataset of the plurality of identificationdatasets associated with the particular blockchain and obtaining theparticular key associated with the particular identification dataset.

The particular key can be provided to a blockchain computing system. Theblockchain computing system can be associated with the particularblockchain. The particular key may be a private key. Alternativelyand/or additionally, the particular key may be a public key. Providingthe particular key can include an intermediary system interfacing withthe blockchain as the user interacts with an abstracted (and/orobfuscated) user interface associated with the profile managementsystem.

In some implementations, the systems and methods can transmit a profilegeneration request to the blockchain computing system. The particularkey can be obtained based on the profile generation request. Key contextdata can then be generated based on the profile generation request. Insome implementations, the key context data can be descriptive of theparticular blockchain and login information associated with theparticular blockchain. The user profile data can then be generated basedat least in part on the particular key and the key context data. Theuser profile data can then be stored.

Additionally and/or alternatively, the systems and methods can includeidentifying a particular set of data of a plurality of datasets that isassociated with a particular blockchain in which the plurality ofdatasets are associated with a plurality of different blockchains. Forexample, the systems and methods can obtain a user verification request.The user verification request can be associated with a particularblockchain of a plurality of different blockchains. In someimplementations, the systems and methods can include obtaining userprofile data based on the user verification request. The user profiledata can be associated with a particular user. In some implementations,the user profile data can be managed with a profile managementextension. The user profile data can include a plurality of blockchainprofile datasets associated with the plurality of different blockchains.The user profile data can be processed to determine a particular keyassociated with the particular blockchain. The particular key can beassociated with a particular blockchain profile dataset of the pluralityof blockchain profile datasets. The particular key can be provided to ablockchain computing system. The blockchain computing system can beassociated with the particular blockchain.

A user verification request can be obtained in response to one or moreuser inputs. The user verification request can be associated with aparticular blockchain of a plurality of different blockchains. Theparticular blockchain can be a decentralized and distributed publicblockchain for storing smart contracts associated with a plurality ofnon-fungible tokens.

The systems and methods can obtain user profile data based on the userverification request. The user profile data can be associated with aparticular user. In some implementations, the user profile data can bemanaged with a profile management extension. Additionally and/oralternatively, the user profile data can include a plurality ofblockchain profile datasets associated with the plurality of differentblockchains. The profile management extension can be a web browserapplication extension. In some implementations, the profile managementextension can be associated with the user profile database. The profilemanagement extension can be associated with one or more applicationprogramming interfaces. The one or more application programminginterfaces can interact with a user interface of a blockchain interface.Additionally and/or alternatively, the user profile data can include aplurality of crypto wallets. Each of the plurality of crypto wallets caninclude access data associated with at least one of a cryptocurrencybalance or a plurality of non-fungible tokens.

The user profile data can be processed to determine a particular keyassociated with the particular blockchain. The particular key can beassociated with a particular blockchain profile dataset of the pluralityof blockchain profile datasets. The particular blockchain profiledataset can include account information associated with the particularblockchain. The account information can include the particular key.Additionally and/or alternatively, the user profile data can includedata associated with a plurality of non-fungible tokens associated witha plurality of different blockchains.

The particular key can be provided to a blockchain computing system. Theblockchain computing system can be associated with the particularblockchain. Blockchain data can then be obtained in response toproviding the particular key. In some implementations, the blockchaindata can include information about the Web3 assets of the particular.

In some implementations, Web2 can refer to the state of the internet notinterdependent on blockchain technology. For example, email platforms,social media platforms, and/or transaction platforms that do notleverage a blockchain can be part of Web2. Web3 can refer to theapplications and other web services that leverage blockchain technology(e.g., decentralized applications that run on the blockchain).Non-fungible tokens and cryptocurrency can be features of Web3.

Additionally and/or alternatively, the one or more keys utilized by thesystems and methods disclosed herein can be a set of data (e.g., anumber, a hash, and/or a function) that is utilized similar to apassword. The one or more keys can be a user indicator to indicate auser's involvement in a transaction and/or in ownership. In someimplementations, a private key can be utilized for authenticating a userfor accessing transactional assets, and a public key can be utilized tosign transactions and show ownership. The one or more keys can include astring (e.g., a set-size bit string).

The systems and methods of the present disclosure provide a number oftechnical effects and benefits. As one example, the system and methodscan provide systems and methods for providing identity management for aplurality of blockchain identities. For example, the systems and methodsdisclosed herein can leverage a profile management system to manage theverification of identities for a plurality of different blockchains bymanaging a plurality of keys.

Another technical benefit of the systems and methods of the presentdisclosure is the ability to leverage the profile management system toprovide an intermediary system for generating a profile for a user whodoes not currently have an account with the platform. For example, thesystems and methods disclosed herein can obtain a request to generate anaccount with a platform (e.g., a blockchain platform) and the systemsand methods can interface with the platform to generate an account inwhich the account information is then stored in the user profiledatabase.

Another example of technical effect and benefit relates to improvedcomputational efficiency and improvements in the functioning of acomputing system. For example, the systems and methods disclosed hereincan leverage the profile management system to reduce the number ofcrypto wallets and/or extensions downloaded in order to interact with aplurality of blockchains.

With reference now to the Figures, example embodiments of the presentdisclosure will be discussed in further detail.

Example Devices and Systems

FIG. 1A depicts a block diagram of an example computing system 100 thatperforms identity management according to example embodiments of thepresent disclosure. The system 100 includes a user computing system 130,a server computing system 110, a creator computing system 150, and ablockchain computing system 170 that are communicatively coupled over anetwork 180.

The user computing system 130 can be any type of computing device, suchas, for example, a personal computing device (e.g., laptop or desktop),a mobile computing device (e.g., smartphone or tablet), a gaming consoleor controller, a wearable computing device, an embedded computingdevice, or any other type of computing device.

The user computing system 130 includes one or more processors 132 and amemory 134. The one or more processors 132 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 134can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 134 can store data 136and instructions 138 which are executed by the processor 132 to causethe user computing system 130 to perform operations.

The user computing system 130 can also include one or more user inputcomponents that receive user input. For example, the user inputcomponent can be a touch-sensitive component (e.g., a touch-sensitivedisplay screen or a touch pad) that is sensitive to the touch of a userinput object (e.g., a finger or a stylus). The touch-sensitive componentcan serve to implement a virtual keyboard. Other example user inputcomponents include a microphone, a traditional keyboard, or other meansby which a user can provide user input.

The server computing system 110 includes one or more processors 112 anda memory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 114 can store data 118and instructions 116 which are executed by the processor 112 to causethe server computing system 110 to perform operations.

In some implementations, the server computing system 110 includes or isotherwise implemented by one or more server computing devices. Ininstances in which the server computing system 110 includes pluralserver computing devices, such server computing devices can operateaccording to sequential computing architectures, parallel computingarchitectures, or some combination thereof.

The blockchain computing system 170 includes one or more processors anda memory. The one or more processors can be any suitable processingdevice (e.g., a processor core, a microprocessor, an ASIC, a FPGA, acontroller, a microcontroller, etc.) and can be one processor or aplurality of processors that are operatively connected. The memory caninclude one or more non-transitory computer-readable storage mediums,such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks,etc., and combinations thereof. The memory can store data andinstructions which are executed by the processor to cause the blockchaincomputing system 170 to perform operations. In some implementations, theblockchain computing system 170 includes or is otherwise implemented byone or more server computing devices.

The network 180 can be any type of communications network, such as alocal area network (e.g., intranet), wide area network (e.g., Internet),or some combination thereof and can include any number of wired orwireless links. In general, communication over the network 180 can becarried via any type of wired and/or wireless connection, using a widevariety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP),encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g.,VPN, secure HTTP, SSL).

The computing system 100 can include a number of applications (e.g.,applications 1 through N). Each application can be in communication witha central intelligence layer. Example applications can include a textmessaging application, an email application, a dictation application, avirtual keyboard application, a browser application, etc. In someimplementations, each application can communicate with the centralintelligence layer (and model(s) stored therein) using an API (e.g., acommon API across all applications).

The central intelligence layer can communicate with a central devicedata layer. The central device data layer can be a centralizedrepository of data for the computing system 100. In someimplementations, the central device data layer can communicate with anumber of other components of the computing device, such as, forexample, one or more sensors, a context manager, a device statecomponent, and/or additional components. In some implementations, thecentral device data layer can communicate with each device componentusing an API (e.g., a private API).

Additionally and/or alternatively, FIG. 1A depicts an exemplarycomputing system 100 that can be used to implement identity managementaccording to aspects of the present disclosure. The system 100 has auser-server architecture that includes a server 110 that communicateswith one or more user computing systems 130 over a network 180. However,the present disclosure can be implemented using other suitablearchitectures, which can include any number of computing systemscommunicating over a network 180.

The system 100 includes a server 110, such as, for example, a webserver. The server 110 can be one or more computing devices that areimplemented as a parallel computing system and/or a distributedcomputing system. In particular, multiple computing devices can acttogether as a single server 110. The server 110 can have one or moreprocessor(s) 112 and a memory 114. The server 110 can also include anetwork interface used to communicate with one or more remote computingdevices (e.g., user devices) 130 over a network 180.

The processor(s) 112 can be any suitable processing device, such as amicroprocessor, microcontroller, integrated circuit, or other suitableprocessing device. The memory 114 can include any suitable computingsystem or media, including, but not limited to, non-transitorycomputer-readable media, RAM, ROM, hard drives, flash drives, or othermemory devices. The memory 114 can store information accessible byprocessor(s) 112, including instructions 116 that can be executed byprocessor(s) 112. The instructions 116 can be any set of instructionsthat when executed by the processor(s) 112, cause the processor(s) 112to provide desired functionality.

In particular, the instructions 116 can be executed by the processor(s)112 to implement index adjustment (e.g., index deduplication). The userprofile database 120 can be configured to store a plurality of userprofiles associated with a plurality of users utilizing one or more usercomputing systems 130. In some implementations, the user profiledatabase 120 can be configured to be utilized for facilitating one ormore interactions. The facilitation of the one or more interactions caninvolve the use of a blockchain application programming interface (API)122 to send data to and receive data from a blockchain computing system170. For example, a server computing system 110 can utilize theblockchain API 122 to update one or more ledgers 172 of the blockchaincomputing system 170. The one or more ledgers 172 can be associated withone or more tokens 174. The one or more tokens 174 can include one ormore non-fungible tokens, which can include scripts associated with adigital asset (e.g., image data, video data, text data, latent encodingdata, domain data, audio data, augmented-reality asset rendering data,and/or virtual-reality asset rendering data). In particular, the scriptcan reference a specific digital asset that is provided for sale. Thedigital asset can include image data, text data, video data, latentencoding data, a domain name, a virtual property, an augmented-realityasset, a virtual-reality asset (e.g., a virtual-reality environmentand/or a virtual-reality object for interaction in an environment), asmart contract, a physical item authentication, etc. In someimplementations, the one or more ledgers 172 can be associated withcryptocurrency that can be utilized to make transactions in a physicalmarketplace and/or a virtual marketplace.

It will be appreciated that the term “element” can refer to computerlogic utilized to provide desired functionality. Thus, any element,function, and/or instructions can be implemented in hardware,application specific circuits, firmware and/or software controlling ageneral purpose processor. In one implementation, the elements orfunctions are program code files stored on the storage device, loadedinto memory and executed by a processor or can be provided from computerprogram products, for example computer executable instructions, that arestored in a tangible computer-readable storage medium such as RAM, harddisk or optical or magnetic media.

Memory 114 can also include data 118 that can be retrieved, manipulated,created, or stored by processor(s) 112. The data 118 can include searchresult data, ranking data, image data (e.g., digital maps, satelliteimages, aerial photographs, street-level photographs, synthetic models,paintings, personal images, portraits, etc.), video data, audio data,text data (e.g., books, articles, blogs, poems, etc.), latent encodingdata, blockchain address data, tables, vector data (e.g., vectorrepresentations of roads, parcels, buildings, etc.), point of interestdata (e.g., locales such as islands, cities, restaurants, hospitals,parks, hotels, and schools), or other data or related information. As anexample, the data 118 can be used to access information and dataassociated with a specific digital asset, website, search result,blockchain, etc.

The data 118 can be stored in one or more databases. The one or moredatabases can be connected to the server 110 by a high bandwidth LAN orWAN, or can also be connected to server 110 through network 180. The oneor more databases can be split up so that they are located in multiplelocales.

The server 110 can exchange data with one or more user computing systems130 over the network 180. Although two user computing systems 130 areillustrated in FIG. 1A, any number of user computing systems 130 can beconnected to the server 110 over the network 180. The user computingsystems 130 can be any suitable type of computing device, such as ageneral purpose computer, special purpose computer, navigational device,laptop, desktop, integrated circuit, mobile device, smartphone, tablet,wearable-computing devices, a display with one or more processorscoupled thereto and/or embedded therein, or other suitable computingdevice. Further, the user computing system 130 can be multiple computingdevices acting together to perform operations or computing actions.

Similar to server 110, a user computing system 130 can include aprocessor(s) 132 and a memory 134. The memory 134 can store informationaccessible by processor(s) 132, including instructions that can beexecuted by processor(s) and data. As an example, memory 134 can storedata 136 and instructions 138.

Instructions 138 can provide instructions for implementing a browser, anon-fungible token purchase, and/or a plurality of other functions. Inparticular, the user of user computing system 130 can exchange data withserver 110 by using the browser to visit a website accessible at aparticular web-address. The identity management of the presentdisclosure can be provided as an element of a user interface of awebsite and/or application.

The data 136 can include data related to running a specializedapplication on the user computing system 130. In particular, thespecialized application can be used to exchange data with server 110over the network 180. The data 136 can include user-device-readable codefor providing and implementing aspects of the present disclosure.Additionally and/or alternatively, the data 136 can include data relatedto previously inputted or received data. For example, the data 136 caninclude data related to past occurrences of the special application.

The user computing system 130 can include various user input devices forreceiving information from a user, such as a touch screen, touch pad,data entry keys, speakers, mouse, motion sensor, and/or a microphonesuitable for voice recognition. Further, the user computing system 130can have a display for presenting information, such as a user interface,displaying a digital asset, displaying pop-ups or application elementsdisplayed in an interface, and/or other forms of information.

The user computing system 130 can also include a user profile 140 thatcan be used to identify a user of the user computing system 130. Theuser profile 140 can be optionally used by the user to make one or moretransactions which can then be recorded on one or more ledgers 172 ofthe blockchain computing system 170. The user profile 140 can bedescriptive of user information, which can include identificationnumbers and/or payment account information. For example, the userprofile 140 can include data associated with a crypto wallet, which maybe linked to a browser application via an application extension and/orembedding.

The user computing system 130 can further include a graphics processingunit. Graphics processing unit can be used by processor 132 to indexadjustment. In some embodiments, the user computing system 130 performsany and all index adjustment.

The user computing system 130 can include a network interface forcommunicating with a server 110 over a network 180. Network interfacecan include any components or configuration suitable for communicationwith server 110 over network 180, including, for example, one or moreports, transmitters, wireless cards, controllers, physical layercomponents, or other items for communication according to any currentlyknown or future developed communications protocol or technology.

The network 180 can be any type of communications network, such as alocal area network (e.g., intranet), wide area network (e.g., Internet),or some combination thereof. The network 180 can also include a directconnection between a user computing system 130 and the server 110. Ingeneral, communication between the server 110 and a user computingsystem 130 can be carried via network interface using any type of wiredand/or wireless connection, using a variety of communication protocols(e.g., TCP/IP, HTTP), encodings or formats (e.g., HTML, XML), and/orprotection schemes (e.g., VPN, secure HTTP, SSL).

In some implementations, the exemplary computing system 100 can includeone or more creator computing systems 150. The one or more creatorcomputing systems 150 can be utilized for generating images, videos,prose, poetry, audio, etc., which can then be provided for sale. The oneor more creator computing systems 150 can include one or more processors152, which can be utilized to execute one or more operations toimplement the systems and methods disclosed herein. The one or morecreator computing systems 150 can include one or more memory components154, which can be utilized to store data 156 and one or moreinstructions 158. The data 156 can include data related to one or moreapplications, one or more media datasets, etc. The instructions 158 caninclude one or more operations for implementing the systems and methodsdisclosed herein.

The one or more creator computing systems 150 can store data associatedwith one or more digital assets 160 and/or one or more creator profiles162. The one or more digital assets 160 can include text data, imagedata, video data, audio data, latent encoding data, domain data, or avariety of other data formats. The one or more creator profiles 162 caninclude information associated with one or more “creators” of the one ormore digital assets 160. The one or more creator profiles 162 caninclude identification data, transaction data, and/or crypto walletdata.

Additionally and/or alternatively, the exemplary computing system 100can include one or more blockchain computing systems 170. The one ormore blockchain computing systems 170 can include a plurality ofcomputing devices being utilized for decentralized data storage, suchthat a plurality of “blocks” can be distributed throughout a network ofcomputing devices to provide a secure system for data storage, which caninclude one or more ledgers 172 and one or more tokens 174. In someimplementations, each of the one or more tokens 174 can be associatedwith at least a portion of the one or more ledgers 172.

Blockchain can refer to a system configured to securely recordinformation. The blockchain can include a decentralized system that canrender changing information extremely difficult. The blockchain caninclude a digital ledger of transactions that can be duplicated anddistributed across a network of computing systems. Each block in thechain can include a number of transactions. When a new transactionoccurs on the blockchain, a record of that transaction can be added toevery computing device's ledger. The blockchain can be utilized to trackthe exchange of currency and/or digital assets via the recording oftransactions on the digital ledger, which can be propagated throughoutthe decentralized system. The currency exchanged and tracked via theblockchain computing system 170 can be referred to as cryptocurrency.

The tokens 174 can include one or more non-fungible tokens. Thenon-fungible tokens can be minted on a blockchain associated with theblockchain computing system 170. A non-fungible token (NFT) can be acertificate of authenticity of a digital asset. NFTs can benon-interchangeable thus making their worth depend on the price anyonemay be willing to pay for the asset. NFTs can be printed on blockchainssuch that their scarcity and authenticity can be maintained. A digitalasset can be defined as anything that is stored digitally and can beuniquely identifiable that organizations can use to realize value.Examples of digital assets can include a tweet, a social media comment,documents, audio, images, videos, logos, website domains, slidepresentations, spreadsheets, CSS files and formats, executable code,and/or websites.

FIG. 1B depicts a block diagram of an example blockchain 50 that may beutilized by the blockchain computing system 170 of the exemplarycomputing system 100 of FIG. 1A. The example blockchain 50 can include aplurality of blocks that can be utilized to store data with one or morecryptographic features. The blockchain 50 can be stored on adecentralized computing system comprising a plurality of computingdevices. The blockchain 50 can be a public blockchain (e.g., ablockchain that is open without access restrictions such that anyonewith an internet access can send transactions or validate transactionsas part of the decentralized, distributed system), a private blockchain(e.g., a blockchain that provides access based on permissions set bynetwork administrators), or a hybrid blockchain (e.g., a blockchain witha combination of blocks with no restrictions and blocks withrestrictions). The blockchain 50 can include proof of work features thatcan include one or more cryptographic forms of proof. The proof of workcan be provided upon a request to update the blockchain 50 (e.g., arequest to update the ledgers based on a new transaction). The proof ofwork can convey that a certain device or group of devices have performeda certain amount of computation, which can then be validated by otherparties. Once validated, the blockchain 50 can be updated, or may remainunchanged in response to a failure to validate. The proof of workfeature can be utilized to mitigate the computational cost of everydevice in the system having to perform the same computational functionsand checks for determining a request is valid for updating theblockchain 50.

Each block can include a hash, a previous hash associated with the hashof the previous block, and data. In some implementations, each block caninclude a nonce. A hash can be a hash value of a fixed length that canbe a fingerprint for the particular block. The hash value can begenerated based on a hash function and may be changed each time a changeis made to the data of that particular block. The previous hash caninclude a hash value of the block immediately preceding the particularblock. The previous hash can be utilized to ensure the downstream groundtruth stays unchanged unless proper validation occurs. The data caninclude transaction data (e.g., a transaction ledger), a timestamp, avalue associated with a cryptocurrency value, a non-fungible token(e.g., a non-fungible token including a script that references a digitalasset, nonce data, and/or general blockchain data. Nonce (i.e., a numberonly used once) can be a number added to a block in a blockchain thatcan meet a difficulty level restriction when a block is rehashed. Thenonce can be a number that blockchain miners are solving for, in orderto receive an incentive (e.g., cryptocurrency).

The blockchain 50 can include one or more security protocols and/orfeatures. The blockchain 50 can include a cryptographic system. Forexample, the blockchain 50 can validate the blockchain 50 is valid byensuring the stored previous hash stored in the block matches the hashvalue of the previous block from the last block back to the first block(e.g., the genesis block). In some implementations, the blockchain 50can include proof of work validation that can rely on verifying proof ofcomputation before implementing a change to the stored data (e.g., thestored ledger). Proof of work validation can take seconds, minutes,and/or hours based in part on the number of blocks in the blockchain 50.Additionally and/or alternatively, the blockchain can be implemented ona distributed, decentralized computing system. In some implementations,each computing device in the distributed, decentralized computing systemcan store a portion of (e.g., a block of the plurality of blocks) or allof the blocks in the blockchain 50. Therefore, the system can verifydata by ensuring the data is uniform across most, if not all, of thedistributed system. Each node of the distributed system can be checkedfor tampering before adding new data.

The data can include data associated with a cryptocurrency value (e.g.,a ledger associated with a specific cryptocurrency value), dataassociated with a digital asset (e.g., a non-fungible token minted onthe blockchain 50 that can include a script associated with the digitalasset), data associated with a smart contract (e.g., a smart contractthat includes conditions that automatically initiates an action inresponse to a criteria being met), and/or timestamp data (e.g.,timestamp data for block creation, minting, a transaction, etc.).

In particular, FIG. 1B depicts a first block 10, a second block 20, athird block 30, a fourth block 40, and an nth block 60. Although fiveblocks are depicted, any number of blocks can be utilized. The firstblock 10 can be a genesis block (e.g., a first overall block in theblockchain). The first block 10 can include a respective first hash 12(e.g., a hash value associated with the first block 10). The first block10 may include a first previous hash 14 (e.g., if the first block 10 hasa block before it in the blockchain 50, then the hash of the previousblock can be stored on the first block 10). Additionally and/oralternatively, the first block 10 can include data 16 and nonce 18.

The second block 20 can follow the first block 10. The second block 20can include a respective second hash 22 (e.g., a hash value associatedwith the second block 20). The second block 20 may include a secondprevious hash 24 (e.g., the second previous hash 24 can be the same as,or reference, the first hash 12). Additionally and/or alternatively, thesecond block 20 can include data 26 and nonce 28.

The third block 30 can follow the second block 20. The third block 30can include a respective third hash 32 (e.g., a hash value associatedwith the third block 30). The third block 30 may include a thirdprevious hash 34 (e.g., the third previous hash 34 can be the same as,or reference, the second hash 22). Additionally and/or alternatively,the third block 30 can include data 36 and nonce 38.

Additionally and/or alternatively, the fourth block 40, the nth block60, and other potential blocks can include a respective hash, arespective previous hash, and data. The first data 16, the second data26, the third data 36, and the data of the other blocks can includeoverlapping data, can differ, and/or be the same such that the data isduplicative for all blocks. In some implementations, each block can beassociated with a different transaction (e.g., a different minting, adifferent sale, etc.). The first nonce 18, the second nonce 28, thethird nonce 38, and the nonce's of the other blocks can differ and maybe solved during mining.

The data in each block can include ledger data, which can include atimestamp, asset and/or cryptocurrency exchanged, actors involved intransaction, and/or a variety of other information.

In some implementations, a plurality of different blockchains can beutilized for the systems and methods disclosed herein. The differentblockchains can include different configurations. The differentblockchains can include parallel chains, side chains, shared blocks,differing chains, varying permissions, varying purposes, varying numberof blocks, and/or varying hash functions and/or varying hashing valuelengths.

In some implementations, the systems and methods can include one or moremachine-learned model computing systems 900. The one or moremachine-learned models can be utilized for a variety of tasks forenabling identity management.

FIG. 9A depicts a block diagram of an example computing system 900 thatperforms identity management according to example embodiments of thepresent disclosure. The system 900 includes a user computing device 902,a server computing system 930, and a training computing system 950 thatare communicatively coupled over a network 980.

The user computing device 902 can be any type of computing device, suchas, for example, a personal computing device (e.g., laptop or desktop),a mobile computing device (e.g., smartphone or tablet), a gaming consoleor controller, a wearable computing device, an embedded computingdevice, or any other type of computing device.

The user computing device 902 includes one or more processors 912 and amemory 914. The one or more processors 912 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 914can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 914 can store data 916and instructions 918 which are executed by the processor 912 to causethe user computing device 902 to perform operations.

In some implementations, the user computing device 902 can store orinclude one or more identity management models 920. For example, theidentity management models 920 can be or can otherwise include variousmachine-learned models such as neural networks (e.g., deep neuralnetworks) or other types of machine-learned models, including non-linearmodels and/or linear models. Neural networks can include feed-forwardneural networks, recurrent neural networks (e.g., long short-term memoryrecurrent neural networks), convolutional neural networks or other formsof neural networks. Example identity management models 920 are discussedwith reference to FIGS. 2 & 3 .

In some implementations, the one or more identity management models 920can be received from the server computing system 930 over network 980,stored in the user computing device memory 914, and then used orotherwise implemented by the one or more processors 912. In someimplementations, the user computing device 902 can implement multipleparallel instances of a single identity management model 920 (e.g., toperform user-specific prediction or suggestion across multiple instancesof third party service providers).

More particularly, the identity management model 920 can include one ormore detection models, one or more segmentation models, one or moreclassification models, one or more augmentation models, one or moregeneration models, and/or one or more feature extractor models. Theidentity management model 920 can process input data to generate asuggestion and/or a prediction specific to the particular user.

Additionally or alternatively, one or more identity management models940 can be included in or otherwise stored and implemented by the servercomputing system 930 that communicates with the user computing device902 according to a client-server relationship. For example, the identitymanagement models 940 can be implemented by the server computing system930 as a portion of a web service (e.g., an identity managementservice). Thus, one or more models 920 can be stored and implemented atthe user computing device 902 and/or one or more models 940 can bestored and implemented at the server computing system 930.

The user computing device 902 can also include one or more user inputcomponents 922 that receive user input. For example, the user inputcomponent 922 can be a touch-sensitive component (e.g., atouch-sensitive display screen or a touch pad) that is sensitive to thetouch of a user input object (e.g., a finger or a stylus). Thetouch-sensitive component can serve to implement a virtual keyboard.Other example user input components include a microphone, a traditionalkeyboard, or other means by which a user can provide user input.

The server computing system 930 includes one or more processors 932 anda memory 934. The one or more processors 932 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 934can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 934 can store data 936and instructions 938 which are executed by the processor 932 to causethe server computing system 930 to perform operations.

In some implementations, the server computing system 930 includes or isotherwise implemented by one or more server computing devices. Ininstances in which the server computing system 930 includes pluralserver computing devices, such server computing devices can operateaccording to sequential computing architectures, parallel computingarchitectures, or some combination thereof.

As described above, the server computing system 930 can store orotherwise include one or more machine-learned identity management models940. For example, the models 940 can be or can otherwise include variousmachine-learned models. Example machine-learned models include neuralnetworks or other multi-layer non-linear models. Example neural networksinclude feed forward neural networks, deep neural networks, recurrentneural networks, and convolutional neural networks. Example models 940are discussed with reference to FIGS. 2 & 3 .

The user computing device 902 and/or the server computing system 930 cantrain the models 920 and/or 940 via interaction with the trainingcomputing system 950 that is communicatively coupled over the network980. The training computing system 950 can be separate from the servercomputing system 930 or can be a portion of the server computing system930.

The training computing system 950 includes one or more processors 952and a memory 954. The one or more processors 952 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 954can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 954 can store data 956and instructions 958 which are executed by the processor 952 to causethe training computing system 950 to perform operations. In someimplementations, the training computing system 950 includes or isotherwise implemented by one or more server computing devices.

The training computing system 950 can include a model trainer 960 thattrains the machine-learned models 920 and/or 940 stored at the usercomputing device 902 and/or the server computing system 930 usingvarious training or learning techniques, such as, for example, backwardspropagation of errors. For example, a loss function can bebackpropagated through the model(s) to update one or more parameters ofthe model(s) (e.g., based on a gradient of the loss function). Variousloss functions can be used such as mean squared error, likelihood loss,cross entropy loss, hinge loss, and/or various other loss functions.Gradient descent techniques can be used to iteratively update theparameters over a number of training iterations.

In some implementations, performing backwards propagation of errors caninclude performing truncated backpropagation through time. The modeltrainer 960 can perform a number of generalization techniques (e.g.,weight decays, dropouts, etc.) to improve the generalization capabilityof the models being trained.

In particular, the model trainer 960 can train the identity managementmodels 920 and/or 940 based on a set of training data 962. The trainingdata 962 can include, for example, training blockchain data, trainingweb page data, training transaction data, ground truth labels, groundtruth information, and/or ground truth segmentation masks.

In some implementations, if the user has provided consent, the trainingexamples can be provided by the user computing device 902. Thus, in suchimplementations, the model 920 provided to the user computing device 902can be trained by the training computing system 950 on user-specificdata received from the user computing device 902. In some instances,this process can be referred to as personalizing the model.

The model trainer 960 includes computer logic utilized to providedesired functionality. The model trainer 960 can be implemented inhardware, firmware, and/or software controlling a general purposeprocessor. For example, in some implementations, the model trainer 960includes program files stored on a storage device, loaded into a memoryand executed by one or more processors. In other implementations, themodel trainer 960 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as RAM hard disk or optical or magnetic media.

The network 980 can be any type of communications network, such as alocal area network (e.g., intranet), wide area network (e.g., Internet),or some combination thereof and can include any number of wired orwireless links. In general, communication over the network 980 can becarried via any type of wired and/or wireless connection, using a widevariety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP),encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g.,VPN, secure HTTP, SSL).

The machine-learned models described in this specification may be usedin a variety of tasks, applications, and/or use cases.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be image data. The machine-learned model(s)can process the image data to generate an output. As an example, themachine-learned model(s) can process the image data to generate an imagerecognition output (e.g., a recognition of the image data, a latentembedding of the image data, an encoded representation of the imagedata, a hash of the image data, etc.). As another example, themachine-learned model(s) can process the image data to generate an imagesegmentation output. As another example, the machine-learned model(s)can process the image data to generate an image classification output.As another example, the machine-learned model(s) can process the imagedata to generate an image data modification output (e.g., an alterationof the image data, etc.). As another example, the machine-learnedmodel(s) can process the image data to generate an encoded image dataoutput (e.g., an encoded and/or compressed representation of the imagedata, etc.). As another example, the machine-learned model(s) canprocess the image data to generate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be text or natural language data. Themachine-learned model(s) can process the text or natural language datato generate an output. As an example, the machine-learned model(s) canprocess the natural language data to generate a language encodingoutput. As another example, the machine-learned model(s) can process thetext or natural language data to generate a latent text embeddingoutput. As another example, the machine-learned model(s) can process thetext or natural language data to generate a classification output. Asanother example, the machine-learned model(s) can process the text ornatural language data to generate a textual segmentation output. Asanother example, the machine-learned model(s) can process the text ornatural language data to generate a semantic intent output. As anotherexample, the machine-learned model(s) can process the text or naturallanguage data to generate an upscaled text or natural language output(e.g., text or natural language data that is higher quality than theinput text or natural language, etc.). As another example, themachine-learned model(s) can process the text or natural language datato generate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be speech data. The machine-learned model(s)can process the speech data to generate an output. As an example, themachine-learned model(s) can process the speech data to generate aspeech recognition output. As another example, the machine-learnedmodel(s) can process the speech data to generate a speech translationoutput. As another example, the machine-learned model(s) can process thespeech data to generate a latent embedding output. As another example,the machine-learned model(s) can process the speech data to generate anencoded speech output (e.g., an encoded and/or compressed representationof the speech data, etc.). As another example, the machine-learnedmodel(s) can process the speech data to generate a textualrepresentation output (e.g., a textual representation of the inputspeech data, etc.). As another example, the machine-learned model(s) canprocess the speech data to generate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be latent encoding data (e.g., a latent spacerepresentation of an input, etc.). The machine-learned model(s) canprocess the latent encoding data to generate an output. As an example,the machine-learned model(s) can process the latent encoding data togenerate a recognition output. As another example, the machine-learnedmodel(s) can process the latent encoding data to generate areconstruction output. As another example, the machine-learned model(s)can process the latent encoding data to generate a search output. Asanother example, the machine-learned model(s) can process the latentencoding data to generate a reclustering output. As another example, themachine-learned model(s) can process the latent encoding data togenerate a prediction output.

In some implementations, the input to the machine-learned model(s) ofthe present disclosure can be statistical data. The machine-learnedmodel(s) can process the statistical data to generate an output. As anexample, the machine-learned model(s) can process the statistical datato generate a recognition output. As another example, themachine-learned model(s) can process the statistical data to generate aprediction output. As another example, the machine-learned model(s) canprocess the statistical data to generate a classification output. Asanother example, the machine-learned model(s) can process thestatistical data to generate a segmentation output. As another example,the machine-learned model(s) can process the statistical data togenerate a segmentation output. As another example, the machine-learnedmodel(s) can process the statistical data to generate a visualizationoutput. As another example, the machine-learned model(s) can process thestatistical data to generate a diagnostic output.

In some cases, the machine-learned model(s) can be configured to performa task that includes encoding input data for reliable and/or efficienttransmission or storage (and/or corresponding decoding). For example,the task may be audio compression task. The input may include audio dataand the output may comprise compressed audio data. In another example,the input includes visual data (e.g., one or more images or videos), theoutput comprises compressed visual data, and the task is a visual datacompression task. In another example, the task may comprise generatingan embedding for input data (e.g., input audio or visual data).

In some cases, the input includes visual data, and the task is acomputer vision task. In some cases, the input includes pixel data forone or more images and the task is an image processing task. Forexample, the image processing task can be image classification, wherethe output is a set of scores, each score corresponding to a differentobject class and representing the likelihood that the one or more imagesdepict an object belonging to the object class. The image processingtask may be object detection, where the image processing outputidentifies one or more regions in the one or more images and, for eachregion, a likelihood that region depicts an object of interest. Asanother example, the image processing task can be image segmentation,where the image processing output defines, for each pixel in the one ormore images, a respective likelihood for each category in apredetermined set of categories. For example, the set of categories canbe foreground and background. As another example, the set of categoriescan be object classes. As another example, the image processing task canbe depth estimation, where the image processing output defines, for eachpixel in the one or more images, a respective depth value. As anotherexample, the image processing task can be motion estimation, where thenetwork input includes multiple images, and the image processing outputdefines, for each pixel of one of the input images, a motion of thescene depicted at the pixel between the images in the network input.

In some cases, the input includes audio data representing a spokenutterance and the task is a speech recognition task. The output maycomprise a text output which is mapped to the spoken utterance. In somecases, the task comprises encrypting or decrypting input data. In somecases, the task comprises a microprocessor performance task, such asbranch prediction or memory address translation.

FIG. 9A illustrates one example computing system that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the user computing device902 can include the model trainer 960 and the training dataset 962. Insuch implementations, the models 920 can be both trained and usedlocally at the user computing device 902. In some of suchimplementations, the user computing device 902 can implement the modeltrainer 960 to personalize the models 920 based on user-specific data.

FIG. 9B depicts a block diagram of an example computing device 970 thatperforms according to example embodiments of the present disclosure. Thecomputing device 970 can be a user computing device or a servercomputing device.

The computing device 970 includes a number of applications (e.g.,applications 1 through N). Each application contains its own machinelearning library and machine-learned model(s). For example, eachapplication can include a machine-learned model. Example applicationsinclude a text messaging application, an email application, a dictationapplication, a virtual keyboard application, a browser application, etc.

As illustrated in FIG. 9B, each application can communicate with anumber of other components of the computing device, such as, forexample, one or more sensors, a context manager, a device statecomponent, and/or additional components. In some implementations, eachapplication can communicate with each device component using an API(e.g., a public API). In some implementations, the API used by eachapplication is specific to that application.

FIG. 9C depicts a block diagram of an example computing device 990 thatperforms according to example embodiments of the present disclosure. Thecomputing device 990 can be a user computing device or a servercomputing device.

The computing device 990 includes a number of applications (e.g.,applications 1 through N). Each application is in communication with acentral intelligence layer. Example applications include a textmessaging application, an email application, a dictation application, avirtual keyboard application, a browser application, etc. In someimplementations, each application can communicate with the centralintelligence layer (and model(s) stored therein) using an API (e.g., acommon API across all applications).

The central intelligence layer includes a number of machine-learnedmodels. For example, as illustrated in FIG. 9C, a respectivemachine-learned model (e.g., a model) can be provided for eachapplication and managed by the central intelligence layer. In otherimplementations, two or more applications can share a singlemachine-learned model. For example, in some implementations, the centralintelligence layer can provide a single model (e.g., a single model) forall of the applications. In some implementations, the centralintelligence layer is included within or otherwise implemented by anoperating system of the computing device 990.

The central intelligence layer can communicate with a central devicedata layer. The central device data layer can be a centralizedrepository of data for the computing device 990. As illustrated in FIG.9C, the central device data layer can communicate with a number of othercomponents of the computing device, such as, for example, one or moresensors, a context manager, a device state component, and/or additionalcomponents. In some implementations, the central device data layer cancommunicate with each device component using an API (e.g., a privateAPI).

Example System Arrangements

FIG. 2 depicts a block diagram of an example profile management system200 according to example embodiments of the present disclosure. In someimplementations, the profile management system 200 can be configured tostore and/or manage one or more management profile datasets 202descriptive of a plurality of account profiles associated with aplurality of users and, as a result of the management of a managementprofile dataset 202, provide access to a plurality of web platformsincluding a first blockchain 212, a second blockchain 214, a socialmedia platform 216, and/or an email platform 218. Thus, in someimplementations, the profile management system 200 can include aplurality of account profiles including a first account profile dataset204, a second account profile dataset 206, a third account profiledataset 208, and/or an nth account profile dataset 210.

In particular, the profile management system 200 can manage one or moremanagement profile datasets 202 associated with one or more users (e.g.,a plurality of management profile datasets in which each of themanagement profile datasets is associated with a different user). Themanagement profile dataset 202 can include a plurality of accountprofile datasets associated with a plurality of different platforms. Forexample, a user can have a plurality of accounts associated with theplurality of different platforms. Therefore, the first account profiledataset 204 can be utilized to access an account for the firstblockchain 212. Additionally and/or alternatively, the second accountprofile dataset 206 can be utilized to access an account for the secondblockchain 214. The third account profile dataset 208 can be utilized toaccess an account for a social media platform 216. In someimplementations, the nth account profile dataset 210 can be utilized toaccess an account for an email platform 218.

FIG. 3 depicts a block diagram of an example key determination system300 according to example embodiments of the present disclosure. Inparticular, the key determination system 300 can process a userverification request 302 to determine a particular key 316 to provideauthenticated access to a blockchain.

For example, a user verification request 302 can be obtained by theprofile management system. The user verification request 302 can beprocessed by an entity determination block 304 to determine a particularblockchain 306 associated with the user verification request. The entitydetermination block 304 can determine a portion of the user verificationrequest 302 is associated with a particular domain, web address, and/orplatform. The particular blockchain 306 can be determined based on theparticular domain, web address, and/or platform.

Additionally and/or alternatively, a user query 308 can be generatedbased on the user verification request 302. The user query 308 can bedescriptive of the particular user associated with the user verificationrequest 302. The user query 308 can be utilized to query the userprofile database 310 to obtain the user profile data 312 associated withthe particular user requesting access to the platform.

The particular blockchain 306 and the user profile data 312 can beprocessed by the identification block 314 to determine the particularkey 316 associated with the particular blockchain 306 and the user. Theidentification block 314 can determine a subset of the user profile data312 is associated with the particular blockchain 306. The subset of theuser profile data 312 can be parsed to obtain the particular key 316.The parsing may include detecting a key in the subset of the userprofile data 312 and segmenting the particular key 316 from the otherdata. The particular key 316 may be identified based on a particulardata structure.

FIG. 4 depicts a block diagram of an example user profile database 400according to example embodiments of the present disclosure. Inparticular, the user profile database 400 can be utilized to store aplurality of user profile datasets associated with a plurality ofdifferent users. For example, the user profile database 400 can store afirst user profile dataset 410 associated with a first user and a seconduser profile dataset 420 associated with a second user. The first userprofile dataset 410 can include data associated with one or more Web3profiles 412, which can include one or more keys 414 associated with theone or more Web3 profiles 412. Additionally and/or alternatively, thefirst user profile dataset 410 can include data associated with one ormore Web2 profiles 416. The one or more Web3 profiles 412 and the one ormore Web2 profiles 416 can be associated with the first user.

The second user profile dataset 420 can include data associated with oneor more Web3 profiles 422, which can include one or more keys 424associated with the one or more Web3 profiles 422. Additionally and/oralternatively, the second user profile dataset 420 can include dataassociated with one or more Web2 profiles 426. The one or more Web3profiles 422 and the one or more Web2 profiles 426 can be associatedwith the second user.

FIG. 5 depicts a block diagram of an example user dashboard 500according to example embodiments of the present disclosure. Inparticular, the user dashboard 500 can provide a display interface for auser to view their assets across different platforms. The user dashboard500 can include different panels for displaying the different assetswith other assets from the same community and/or from the sameblockchain. Alternatively and/or additionally, the user dashboard 500can provide the different assets for display together and/or via anotherdisplay categorization (e.g., alphabetically, the type of digital asset(e.g., image, video, audio, etc.).

For example, the user dashboard 500 can include a first panel for afirst blockchain 510, a second panel for the second blockchain 520,and/or a third panel for the third blockchain 530. One or more firstnon-fungible tokens 512 associated with the first blockchain 510 ownedby the user can be provided for display in the first panel. Additionallyand/or alternatively, data associated with a user first cryptocurrencybalance 516 associated with the first blockchain 510 can be provided fordisplay in the first panel.

One or more second non-fungible tokens 522 associated with the secondblockchain 520 owned by the user can be provided for display in thesecond panel. Additionally and/or alternatively, data associated with auser second cryptocurrency balance 526 associated with the secondblockchain 520 can be provided for display in the second panel.

One or more third non-fungible tokens 532 associated with thirdblockchain 530 owned by the user can be provided for display in thethird panel. Additionally and/or alternatively, data associated with auser third cryptocurrency balance 536 associated with the thirdblockchain 530 can be provided for display in the third panel.

Example Methods

FIG. 6 depicts a flow chart diagram of an example method to performaccording to example embodiments of the present disclosure. AlthoughFIG. 6 depicts steps performed in a particular order for purposes ofillustration and discussion, the methods of the present disclosure arenot limited to the particularly illustrated order or arrangement. Thevarious steps of the method 600 can be omitted, rearranged, combined,and/or adapted in various ways without deviating from the scope of thepresent disclosure.

At 602, a computing system can obtain a user verification request. Theuser verification request can be associated with a particular blockchainof a plurality of different blockchains. In some implementations,obtaining the user verification request can be based on providing inputdata to a blockchain computing system. The user verification request canbe obtained from a blockchain computing system. Alternatively and/oradditionally, the user verification request may be obtained from ablockchain computing system. The user verification request can bedescriptive of a request for one or more particular keys associated witha particular blockchain. Additionally and/or alternatively, the userverification request can include a request for account information(e.g., an account ID, a username, an email, a password, a hash, and/oran IP address).

At 604, the computing system can obtain user profile data based on theuser verification request. The user profile data can be associated witha particular user. In some implementations, the user profile data caninclude a plurality of keys associated with the plurality of differentblockchains. The user profile data can be stored by a server computingsystem. In some implementations, the user verification request can beobtained via a web browser application. Additionally and/oralternatively, providing the particular key can include providing theparticular key via an application extension. The application extensioncan be a browser extension that interfaces with a browser application.Alternatively and/or additionally, providing the particular key caninclude instructing an application programming interface to interfacewith the particular blockchain. In some implementations, the userprofile data can include login information for a plurality of webplatforms. The user profile data can be managed by a profile managementsystem that facilitates the storage, retrieval, and transmission of oneor more private keys. In some implementations, the plurality of keys canbe stored with one or more cryptographic techniques.

In some implementations, obtaining the user profile data based on theuser verification request can include accessing a user profile database.The user profile database can include profile information for aplurality of different users. Additionally and/or alternatively, theuser profile database can be accessed via a profile management system.The profile management system can include an intermediary system forobtaining user information and interacting with a plurality of differentweb platforms and/or account services.

At 606, the computing system can determine a particular key of theplurality of keys associated with the particular blockchain. Determiningthe particular key can include processing the user profile data todetermine a subset of the user profile data that is associated with theparticular blockchain. The subset of the user profile data can then beprocessed to identify the particular key. In some implementations, thedetermination can be performed by a machine-learned model. Alternativelyand/or additionally, the determination can be performed based on one ormore deterministic functions and/or heuristics. The identification canbe based on the data structure associated with the particular key and/orbased on one or more annotations or indexing techniques.

In some implementations, determining the particular key of the pluralityof keys associated with the particular blockchain can includedetermining the particular blockchain based on the user verificationrequest and determining the particular key based on the particularblockchain.

At 608, the computing system can provide the particular key to ablockchain computing system. The blockchain computing system can beassociated with the particular blockchain. Providing the particular keycan include causing an application programming interface to interfacewith the blockchain computing system.

In some implementations, the systems and methods can include obtainingblockchain data from the blockchain computing system in response toproviding the particular key to the blockchain computing system. Theblockchain data may be obtained via an application programming interfaceand/or via a blockchain node.

Alternatively and/or additionally, the systems and methods can includeobtaining one or more user inputs. The systems and methods can determinethe one or more user inputs are associated with a blockchain transactionrequest. The blockchain transaction request can be provided to theblockchain computing system. Transaction data can then be obtained inresponse to providing the particular key and the blockchain transactionrequest.

FIG. 7 depicts a flow chart diagram of an example method to performaccording to example embodiments of the present disclosure. AlthoughFIG. 7 depicts steps performed in a particular order for purposes ofillustration and discussion, the methods of the present disclosure arenot limited to the particularly illustrated order or arrangement. Thevarious steps of the method 700 can be omitted, rearranged, combined,and/or adapted in various ways without deviating from the scope of thepresent disclosure.

At 702, a computing system can generate a profile generation request andtransmit the profile generation request to a blockchain computingsystem. The profile generation request can be generated in response to auser interacting with a third party platform for the first time. Forexample, a user can request to interact with a new blockchain (e.g.,requests to set-up a smart contract and/or requests to trigger a smartcontract on the blockchain). Generating the profile generation requestcan include causing one or more application programming interfaces toprocess a profile generation page associated with the web platform, todetermine the criteria requested by the web platform, to obtain thecriteria data for the particular user, and to generate the profilegeneration request based on the obtained criteria data. The transmissionof the data to the blockchain computing system can include providing anintermediary system that interfaces with the profile generation page toinput the obtained criteria data into the one or more input fields.

At 704, the computing system can obtain a particular key based on theprofile generation request and generate key context data based on theprofile generation request. The particular key can be a key associatedwith the particular blockchain (e.g., a private key and/or a public keyfor accessing the blockchain assets associated with the particularuser). The key context data can include one or more hashes, one or moreidentifiers associated with the particular blockchain (e.g., one or morelabels and/or one or more annotations), and/or one or more token IDs.

At 706, the computing system can generate user profile data based atleast in part on the particular key and the key context data. The userprofile data can be structured to be parsed in order to obtain relevantdata based on the type of data.

At 708, the computing system can store the user profile data. The userprofile data can be stored in a user profile database. The user profiledatabase can include user profile data for a plurality of users.

At 710, the computing system can obtain a user verification request andobtain the user profile data based on the user verification request. Theuser verification request can be associated with a particularblockchain. The user verification request can be associated with a loginportal associated with the particular blockchain. In someimplementations, the user verification request can be generated based onthe detection of one or more input fields in the login portal. The userverification request can be descriptive of a request for an input foreach of the one or more input fields.

In some implementations, the user profile data can be associated with aparticular user. In some implementations, the user profile data caninclude a plurality of identification datasets associated with theplurality of different web platforms. The plurality of different webplatforms can include a plurality of different blockchain platforms. Insome implementations, the plurality of different web platforms caninclude one or more social media platforms. Additionally and/oralternatively, each identification dataset of the plurality ofidentification datasets can include login information associated with arespective web platform. An identification dataset associated with ablockchain can include one or more keys (e.g., one or more private keysand/or one or more public keys), one or more token IDs, one or morehashes, and/or one or more asset-specific datasets (e.g., dataassociated with non-fungible tokens and/or cryptocurrency). Additionallyand/or alternatively, the user profile data can include an email addressand/or other communication data. The email address and/or the othercommunication data can be leveraged for one or more users to email otherusers associated with a particular non-fungible token community. Thecommunication data can therefore be leveraged for community building.

In some implementations, a profile management system can provide acommunication interface that obtains communication requests from a firstuser, determines an intended audience, obtains communication dataassociated with the intended audience (e.g., one or more second users),and provides a communication notification to the intended audience. Thecommunication can be performed without revealing the communication dataof the first user or the one or more second users. The profilemanagement system can therefore keep the communication data private,while still allowing users to build a community.

At 712, the computing system can process the user profile data todetermine the particular key associated with the particular blockchainand provide the particular key to a blockchain computing system. In someimplementations, processing the user profile data to determine theparticular key associated with the particular blockchain can includedetermining a particular identification dataset of the plurality ofidentification datasets associated with the particular blockchain andobtaining the particular key associated with the particularidentification dataset.

In some implementations, the blockchain computing system can beassociated with the particular blockchain. The particular key may be aprivate key. Alternatively and/or additionally, the particular key maybe a public key. Providing the particular key can include anintermediary system interfacing with the blockchain as the userinteracts with an abstracted (and/or obfuscated) user interfaceassociated with the profile management system.

In some implementations, the systems and methods can transmit a profilegeneration request to the blockchain computing system. The particularkey can be obtained based on the profile generation request. Key contextdata can then be generated based on the profile generation request. Insome implementations, the key context data can be descriptive of theparticular blockchain and login information associated with theparticular blockchain. The user profile data can then be generated basedat least in part on the particular key and the key context data. Theuser profile data can then be stored.

FIG. 8 depicts a flow chart diagram of an example method to performaccording to example embodiments of the present disclosure. AlthoughFIG. 8 depicts steps performed in a particular order for purposes ofillustration and discussion, the methods of the present disclosure arenot limited to the particularly illustrated order or arrangement. Thevarious steps of the method 800 can be omitted, rearranged, combined,and/or adapted in various ways without deviating from the scope of thepresent disclosure.

At 802, a computing system can obtain a user verification request. Theuser verification request can be associated with a particular blockchainof a plurality of different blockchains. The particular blockchain canbe a decentralized and distributed public blockchain for storing smartcontracts associated with a plurality of non-fungible tokens.

At 804, the computing system can obtain user profile data based on theuser verification request. The user profile data can be associated witha particular user. In some implementations, the user profile data can bemanaged with a profile management extension. Additionally and/oralternatively, the user profile data can include a plurality ofblockchain profile datasets associated with the plurality of differentblockchains. The profile management extension can be a web browserapplication extension. In some implementations, the profile managementextension can be associated with the user profile database. The profilemanagement extension can be associated with one or more applicationprogramming interfaces. The one or more application programminginterfaces can interact with a user interface of a blockchain interface.Additionally and/or alternatively, the user profile data can include aplurality of crypto wallets. Each of the plurality of crypto wallets caninclude access data associated with at least one of a cryptocurrencybalance or a plurality of non-fungible tokens.

At 806, the computing system can process the user profile data todetermine a particular key associated with the particular blockchain andprovide the particular key to a blockchain computing system. Theparticular key can be associated with a particular blockchain profiledataset of the plurality of blockchain profile datasets. The particularblockchain profile dataset can include account information associatedwith the particular blockchain. The account information can include theparticular key. Additionally and/or alternatively, the user profile datacan include data associated with a plurality of non-fungible tokensassociated with a plurality of different blockchains.

In some implementations, the blockchain computing system can beassociated with the particular blockchain. Blockchain data can then beobtained in response to providing the particular key. In someimplementations, the blockchain data can include information about theWeb3 assets of the particular user.

At 808, the computing system can obtain one or more user inputs anddetermine the one or more user inputs are associated with a blockchaintransaction request. The one or more user inputs can be obtained via oneor more selections to a user interface. The user interface may beassociated with the blockchain. Alternatively and/or additionally, theuser interface may be provided by the profile management system as partof an intermediary system.

At 810, the computing system can provide the blockchain transactionrequest to the blockchain computing system. Providing the blockchaintransaction request can include triggering the transfer of a payload ofa smart contract.

At 812, the computing system can obtain transaction data in response toproviding the particular key and the blockchain transaction request. Thetransaction data can include data associated with a non-fungible tokentransaction. Additionally and/or alternatively, the transaction data caninclude data associated with a cryptocurrency transaction.

Additional Disclosure

The technology discussed herein makes reference to servers, databases,software applications, and other computer-based systems, as well asactions taken and information sent to and from such systems. Theinherent flexibility of computer-based systems allows for a greatvariety of possible configurations, combinations, and divisions of tasksand functionality between and among components. For instance, processesdiscussed herein can be implemented using a single device or componentor multiple devices or components working in combination. Databases andapplications can be implemented on a single system or distributed acrossmultiple systems. Distributed components can operate sequentially or inparallel.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

What is claimed is:
 1. A computing system, the system comprising: one ormore processors; and one or more non-transitory computer-readable mediathat collectively store instructions that, when executed by the one ormore processors, cause the computing system to perform operations, theoperations comprising: obtaining a user verification request, whereinthe user verification request is associated with a particular blockchainof a plurality of different blockchains; obtaining user profile databased on the user verification request, wherein the user profile data isassociated with a particular user, and wherein the user profile datacomprises a plurality of keys associated with the plurality of differentblockchains; determining a particular key of the plurality of keysassociated with the particular blockchain; and providing the particularkey to a blockchain computing system, wherein the blockchain computingsystem is associated with the particular blockchain.
 2. The system ofclaim 1, wherein the operations further comprise: in response toproviding the particular key to the blockchain computing system,obtaining blockchain data from the blockchain computing system.
 3. Thesystem of claim 1, wherein the user profile data is stored by a servercomputing system, wherein the user verification request is obtained viaa web browser application, and wherein providing the particular keycomprises providing the particular key via an application extension. 4.The system of claim 1, wherein the operations further comprise:obtaining one or more user inputs; determining the one or more userinputs are associated a blockchain transaction request; providing theblockchain transaction request to the blockchain computing system; andobtaining transaction data in response to providing the particular keyand the blockchain transaction request.
 5. The system of claim 1,wherein the user profile data comprises login information for aplurality of web platforms.
 6. The system of claim 1, wherein the userprofile data is managed by a profile management system that facilitatesthe storage, retrieval, and transmission of one or more private keys. 7.The system of claim 1, wherein the plurality of keys are stored with oneor more cryptographic techniques.
 8. The system of claim 1, whereinobtaining the user verification request is based on providing input datato a blockchain computing system.
 9. The system of claim 1, whereinobtaining the user profile data based on the user verification requestcomprises: accessing a user profile database, wherein the user profiledatabase comprises profile information for a plurality of differentusers.
 10. The system of claim 1, wherein determining the particular keyof the plurality of keys associated with the particular blockchaincomprises: determining the particular blockchain based on the userverification request; and determining the particular key based on theparticular blockchain.
 11. A computer-implemented method, the methodcomprising: obtaining, by a computing system comprising one or moreprocessors, a user verification request, wherein the user verificationrequest is associated with a particular blockchain; obtaining, by thecomputing system, user profile data based on the user verificationrequest, wherein the user profile data is associated with a particularuser, and wherein the user profile data comprises a plurality ofidentification datasets associated with the plurality of different webplatforms; processing, by the computing system, the user profile data todetermine a particular key associated with the particular blockchain;and providing, by the computing system, the particular key to ablockchain computing system, wherein the blockchain computing system isassociated with the particular blockchain.
 12. The method of claim 11,wherein the operations further comprise: transmitting a profilegeneration request to the blockchain computing system; obtaining theparticular key based on the profile generation request; generating keycontext data based on the profile generation request, wherein the keycontext data is descriptive of the particular blockchain and logininformation associated with the particular blockchain; generating theuser profile data based at least in part on the particular key and thekey context data; and storing the user profile data.
 13. The method ofclaim 11, wherein processing, by the computing system, the user profiledata to determine the particular key associated with the particularblockchain comprises: determining a particular identification dataset ofthe plurality of identification datasets associated with the particularblockchain; and obtaining the particular key associated with theparticular identification dataset.
 14. The method of claim 11, whereinthe plurality of different web platforms comprise a plurality ofdifferent blockchain platforms.
 15. The method of claim 11, wherein theplurality of different web platforms comprise one or more social mediaplatforms.
 16. The method of claim 11, wherein each identificationdataset of the plurality of identification datasets comprise logininformation associated with a respective web platform.
 17. One or morenon-transitory computer-readable media that collectively storeinstructions that, when executed by one or more computing devices, causethe one or more computing devices to perform operations, the operationscomprising: obtaining a user verification request, wherein the userverification request is associated with a particular blockchain of aplurality of different blockchains; obtaining user profile data based onthe user verification request, wherein the user profile data isassociated with a particular user, and wherein the user profile data ismanaged with a profile management extension, wherein the user profiledata comprises a plurality of blockchain profile datasets associatedwith the plurality of different blockchains; processing the user profiledata to determine a particular key associated with the particularblockchain, wherein the particular key is associated with a particularblockchain profile dataset of the plurality of blockchain profiledatasets; and providing the particular key to a blockchain computingsystem, wherein the blockchain computing system is associated with theparticular blockchain.
 18. The one or more non-transitorycomputer-readable media of claim 17, wherein the profile managementextension is a web browser application extension, and wherein profilemanagement extension is associated with user profile database.
 19. Theone or more non-transitory computer-readable media of claim 17, whereinthe profile management extension is associated with one or moreapplication programming interfaces, wherein the one or more applicationprogramming interfaces interact with a user interface of a blockchaininterface.
 20. The one or more non-transitory computer-readable media ofclaim 17, wherein the user profile data comprises a plurality of cryptowallets, wherein each of the plurality of crypto wallets comprise accessdata associated with at least one of a cryptocurrency balance or aplurality of non-fungible tokens.